Results 71 to 80 of about 14,713 (239)

Beyond Prediction: Data, Baselines, Explanation, and Causation in Machine Learning for Food Insecurity

Food Safety and Health, EarlyView.
The gains from machine learning in nowcasting and forecasting food insecurity are still small and limited and cannot be observed in all countries. This review summarizes the public resources for data, corrects common misconceptions about model requirements, and establishes baseline requirements, explanations, causal inference, and equity in operational
Shabnam Mehboob, Azman Abdullah, Gunesh Kumar, Samreen Memon, Naila Afghan   +4 more
wiley   +1 more source

High-Dimensional Aerodynamic Modeling Prediction Based on Modified RBF Neural Network with Data Assimilation

气体物理
In this paper, the radial basis function (RBF) neural network was modified by data assimilation method to improve the modeling accuracy of high-dimensional aerodynamics. A correction factor γ was introduced into the kernel function of the traditional RBF
Ying ZHANG, Xin ZHANG, Weiguo ZHANG, Zichen DENG   +3 more
doaj   +1 more source

Short‐Term Agricultural Drought Prediction Using Explainable Data‐Driven Models and Quantified Uncertainty

International Journal of Climatology, EarlyView.
This study demonstrates that explainable deep learning models can skilfully predict short‐term agricultural drought using standardized drought indices, capturing hydroclimatic memory and seasonal persistence. The integration of uncertainty quantification and SHAP‐based interpretability reveals the dominant role of antecedent moisture and climate ...
K. Saranya Das, N. R. Chithra, Venkataramana Sridhar   +2 more
wiley   +1 more source

Phase transmittance RBF neural networks

Electronics Letters, 2007
Presented is a new complex valued radial basis function (RBF) neural network with phase transmittance between the input nodes and output, which makes it suitable for channel equalisation on quadrature digital modulation systems.
D.V. Loss, M.C.F. DeCastro, P.R.G. Franco, F.C.C. DeCastro   +3 more
openaire   +1 more source

High‐Fidelity Synthetic Raman Spectra Generation for Sinter Basicity Prediction Using β$$ \beta $$‐Variational Autoencoders

Journal of Raman Spectroscopy, EarlyView.
A β$$ \beta $$‐variational autoencoder with β$$ \beta $$ = 0.1 generates high‐fidelity synthetic Raman spectra of industrial sinter with a 16‐fold improvement in spectral fidelity over SMOTE‐based augmentation (KL divergence: 0.0075 vs. 0.121), enabling reliable basicity prediction (R2 = 0.83) from limited labeled datasets.
Marjorie Ariele Pereira, Daniel Cruz Cavalieri, Adilson Ribeiro Prado, Cassius Zanetti Resende, Erica Simões Rodrigues   +4 more
wiley   +1 more source

A New Localization Algorithm Based on Taylor Series Expansion for NLOS Environment

Cybernetics and Information Technologies, 2016
In Non-Line-Of-Sight (NLOS) environment, location accuracy of Taylorseries expansion location algorithm degrades greatly. A new Taylor-series expansion location algorithm based on self-adaptive Radial-Basis-Function (RBF) neural network is proposed in ...
Ren Jin, Chen Jingxing, Bai Wenle
doaj   +1 more source

RBF-MLMR: A Multi-Label Metamorphic Relation Prediction Approach Using RBF Neural Network

IEEE Access, 2017
Metamorphic testing has been successfully used in many different fields to solve the test oracle problem. However, how to find a set of appropriate metamorphic relations for metamorphic testing remains a complicated and tedious task.
Pengcheng Zhang, Xuewu Zhou, Patrizio Pelliccione, Hareton Leung   +3 more
doaj   +1 more source

From Planar to 3D Nanophotonic Lenses: Advancing Design, Fabrication, and Applications

Laser &Photonics Reviews, EarlyView.
Three‐dimensional nanophotonic lenses are enabled by an integrated workflow that links design parameterization, full‐wave electromagnetic simulation, computational optimization, and freeform fabrication. This Review summarizes how these tools expand optical design freedom beyond a single patterned plane by enriching the local meta‐atom response and ...
Wei Zhu, Lyudmila V. Goncharova, Eric Johlin   +2 more
wiley   +1 more source

Novel Ensemble Prediction Framework for Fast and Accurate Phase Prediction in FeNiCoCrAlCu High‐Entropy Alloys by Integrating Data Augmentation and Interpretable Methods

Materials Genome Engineering Advances, EarlyView.
A physics‐informed machine learning framework is established for phase prediction in FeNiCoCrAlCu HEA. Limited experimental data are expanded through GMM‐based augmentation, followed by descriptor calculation, Pearson‐PCA feature optimization, stacking ensemble classification, and SHAP interpretation, enabling accurate phase identification and ...
Ruixiao Zhang, Biao Chen, Ruihao Yuan, Yi Williams Wang, Jun Wang, Jinshan Li   +5 more
wiley   +1 more source

Energy Transfer and Saving in a Nonlinear Coupled Vibrating System Based on the Method of Composite Synchronization Transmission

International Journal of Mechanical System Dynamics, EarlyView.
ABSTRACT The vibrating screening equipment in industry usually requires power supply to each driving motor to keep up with its normal operation. The self‐synchronization transmission motion of two motors can achieve the screening requirement and save electrical energy by driving one single inductor motor. Nevertheless, this technology is currently only
Lei Jia, Di Zhang, Dawei Gu
wiley   +1 more source